Identifying data-driven subtypes of major depressive disorder with electronic health records

BACKGROUND

Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration.

METHODS

We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups.

RESULTS

Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %).

LIMITATIONS

Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions.

CONCLUSION

These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

Emotional subtypes in patients with depression: A cluster analysis

Major depressive disorder (MDD) is associated with deficits in emotion experience, expression and regulation. Whilst emotion regulation deficits prolong MDD, emotion expression influences symptomatic presentations, and anticipatory pleasure deficits predict recurrence risk. Profiling MDD patients from an emotion componential perspective can characterize subtypes with different clinical and functional outcomes. This study aimed to investigate emotional subtypes of MDD. A two-stage cluster analysis applied to 150 MDD patients. Clustering variables included emotion experience measured by Temporal Experience of Pleasure Scale, emotion expression measured by Toronto Alexithymia Scale, and emotion regulation measured by Emotion Regulation Questionnaire. We validated the resultant clusters by comparing their symptoms and functioning with that of 50 controls. Cluster 1 (n = 50) exhibited intact emotion experience and expression yet adopted reappraisal rather than suppression strategy, whereas Cluster 2 (n = 66) exhibited generalized emotional deficits. Cluster 3 (n = 34) exhibited emotion expression deficits and adopted both reappraisal and suppression strategies. On validation, Cluster 2 exhibited the worst, but Cluster 1 exhibited the least symptoms and social functioning impairments. Cluster 3 was intermediate among the two other subtypes. Our findings support the existence of different emotional subtypes in MDD patients, and have clinical and theoretical implications for developing future specific treatments for MDD.

The DSM-5 Clinical and Public Health Committee (CPHC): operations, mechanics, controversies and recommendations

BACKGROUND

For DSM - 5, the American Psychiatric Association Board of Trustees established a robust vetting and review process that included two review committees that did not exist in the development of prior DSMs, the Scientific Review Committee (SRC) and the Clinical and Public Health Committee (CPHC). The CPHC was created as a body that could independently review the clinical and public health merits of various proposals that would fall outside of the strictly defined scientific process.

METHODS

This article describes the principles and issues which led to the creation of the CPHC, the composition and vetting of the committee, and the processes developed by the committee - including the use of external reviewers.

RESULTS

Outcomes of some of the more involved CPHC deliberations, specifically, decisions concerning elements of diagnoses for major depressive disorder, autism spectrum disorder, catatonia, and substance use disorders, are described. The Committee's extensive reviews and its recommendations regarding Personality Disorders are also discussed.

CONCLUSIONS

On the basis of our experiences, the CPHC membership unanimously believes that external review processes to evaluate and respond to Work Group proposals is essential for future DSM efforts. The Committee also recommends that separate SRC and CPHC committees be appointed to assess proposals for scientific merit and for clinical and public health utility and impact.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

Fatigue and brain arousal in patients with major depressive disorder

Fatigue is considered a key symptom of major depressive disorder (MDD), yet the term lacks specificity. It can denote a state of increased sleepiness and lack of drive (i.e., downregulated arousal) as well as a state of high inner tension and inhibition of drive with long sleep onset latencies (i.e., upregulated arousal), the latter typically found in depression. It has been proposed to differentiate fatigue along the dimension of brain arousal. We investigated whether such stratification within a group of MDD patients would reveal a subgroup with distinct clinical features. Using an automatic classification of EEG vigilance stages, an arousal stability score was calculated for 15-min resting EEGs of 102 MDD patients with fatigue. 23.5% of the patients showed signs of hypoarousal with EEG patterns indicating drowsiness or sleep; this hypoaroused subgroup was compared with remaining patients (non-hypoaroused subgroup) concerning self-rated measures of depressive symptoms, sleepiness, and sleep. The hypoaroused subgroup scored higher on the Beck Depression Inventory items "loss of energy" (Z = - 2.13, p = 0.033; ɳ² = 0.044, 90% CI 0.003-0.128) and "concentration difficulty" (Z = - 2.40, p = 0.017; ɳ² = 0.056, 90% CI 0.009-0.139), and reported higher trait and state sleepiness (p < 0.05) as compared to the non-hypoaroused group. The non-hypoaroused subgroup, in contrast, reported more frequently the presence of suicidal ideation (Chi² = 3.81, p = 0.051; ɳ² = 0.037, 90% CI 0.0008-0.126). In this study, we found some evidence that stratifying fatigued MDD patients by arousal may lead to subgroups that are pathophysiologically and clinically more homogeneous. Brain arousal may be a worth while target in clinical research for better understanding the mechanisms underlying suicidal tendencies and to improve treatment response.

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Accuracy of the PHQ-2 Alone and in Combination With the PHQ-9 for Screening to Detect Major Depression: Systematic Review and Meta-analysis

IMPORTANCE

The Patient Health Questionnaire depression module (PHQ-9) is a 9-item self-administered instrument used for detecting depression and assessing severity of depression. The Patient Health Questionnaire-2 (PHQ-2) consists of the first 2 items of the PHQ-9 (which assess the frequency of depressed mood and anhedonia) and can be used as a first step to identify patients for evaluation with the full PHQ-9.

OBJECTIVE

To estimate PHQ-2 accuracy alone and combined with the PHQ-9 for detecting major depression.

DATA SOURCES

MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO, and Web of Science (January 2000-May 2018).

STUDY SELECTION

Eligible data sets compared PHQ-2 scores with major depression diagnoses from a validated diagnostic interview.

DATA EXTRACTION AND SYNTHESIS

Individual participant data were synthesized with bivariate random-effects meta-analysis to estimate pooled sensitivity and specificity of the PHQ-2 alone among studies using semistructured, fully structured, or Mini International Neuropsychiatric Interview (MINI) diagnostic interviews separately and in combination with the PHQ-9 vs the PHQ-9 alone for studies that used semistructured interviews. The PHQ-2 score ranges from 0 to 6, and the PHQ-9 score ranges from 0 to 27.

RESULTS

Individual participant data were obtained from 100 of 136 eligible studies (44 318 participants; 4572 with major depression [10%]; mean [SD] age, 49 [17] years; 59% female). Among studies that used semistructured interviews, PHQ-2 sensitivity and specificity (95% CI) were 0.91 (0.88-0.94) and 0.67 (0.64-0.71) for cutoff scores of 2 or greater and 0.72 (0.67-0.77) and 0.85 (0.83-0.87) for cutoff scores of 3 or greater. Sensitivity was significantly greater for semistructured vs fully structured interviews. Specificity was not significantly different across the types of interviews. The area under the receiver operating characteristic curve was 0.88 (0.86-0.89) for semistructured interviews, 0.82 (0.81-0.84) for fully structured interviews, and 0.87 (0.85-0.88) for the MINI. There were no significant subgroup differences. For semistructured interviews, sensitivity for PHQ-2 scores of 2 or greater followed by PHQ-9 scores of 10 or greater (0.82 [0.76-0.86]) was not significantly different than PHQ-9 scores of 10 or greater alone (0.86 [0.80-0.90]); specificity for the combination was significantly but minimally higher (0.87 [0.84-0.89] vs 0.85 [0.82-0.87]). The area under the curve was 0.90 (0.89-0.91). The combination was estimated to reduce the number of participants needing to complete the full PHQ-9 by 57% (56%-58%).

CONCLUSIONS AND RELEVANCE

In an individual participant data meta-analysis of studies that compared PHQ scores with major depression diagnoses, the combination of PHQ-2 (with cutoff ≥2) followed by PHQ-9 (with cutoff ≥10) had similar sensitivity but higher specificity compared with PHQ-9 cutoff scores of 10 or greater alone. Further research is needed to understand the clinical and research value of this combined approach to screening.

Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis

BACKGROUND

Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.

METHODS

We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.

RESULTS

16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).

CONCLUSIONS

PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Quantifying the level of difficulty to treat major depressive disorder with antidepressants: Treatment Resistance to Antidepressants Evaluation Scale

BACKGROUND

The present study aimed to develop a new scale to evaluate the level of difficulty in treating major depressive disorder with antidepressants based on the lifetime treatment profile.

METHODS

In addition to evaluating the difficulty of treatment with antidepressants (A subscale), the Treatment Resistance to Antidepressants Evaluation Scale (TRADES) is comprised of a subscale to account for the attributes that compromise the efficacy of treatment (B subscale). One hundred and six participants aged 18 to 65 years with remitted major depressive disorder were enrolled. Eligible cases were those with at least 2 years from disease onset until the scoring date of the TRADES (the index date), with a complete treatment record. Various psychosocial and clinical features, such as neuroticism, harm avoidance, and utilization of psychiatric services, were used to validate the TRADES.

RESULTS

The mean duration of the course before and after the index date were 5.5 ± 3.5 and 3.1 ± 1.7 years, respectively. In a multiple regression analysis, the final total scores of the TRADES independently correlated with higher levels of neuroticism and harm avoidance. Total scores were also associated with a higher utilization of psychiatric outpatient and admission services before the index date. Furthermore, it is thought that total scores could predict a higher number of visits to psychiatric outpatient, emergency, and admission services following the index date.

CONCLUSIONS

The TRADES has acceptable validity and could help to quantify the level of treatment difficulty with antidepressants in major depressive disorder.

Depression Severity Classification from Speech Emotion

Major Depressive Disorder (MDD) is a common psychiatric illness. Automatically classifying depression severity using audio analysis can help clinical management decisions during Deep Brain Stimulation (DBS) treatment of MDD patients. Leveraging the link between short-term emotions and long-term depressed mood states, we build our predictive model on the top of emotion-based features. Because acquiring emotion labels of MDD patients is a challenging task, we propose to use an auxiliary emotion dataset to train a Deep Neural Network (DNN) model. The DNN is then applied to audio recordings of MDD patients to find their low dimensional representation to be used in the classification algorithm. Our preliminary results indicate that the proposed approach, in comparison to the alternatives, effectively classifies depressed and improved phases of DBS treatment with an AUC of 0.80.

A critical analysis of debates on grief and depressive disorder in the age of the Diagnostic and Statistical Manual of Mental Disorders

Since the incorporation of the major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) in 1980, and until its update in the DSM-IV-TR, the DSM classification system considered it necessary to include the criterion of "bereavement exclusion", with the aim of differentiating normal sadness linked to a loss, from a mental disorder, such as the major depressive disorder. In its latest version (DSM-5), this exception was removed, giving rise to a controversy that continues to this day. The debate has set those who are in favor of maintaining this exclusion and extending it to other stressors against those who have intended to eradicate it. Our hypothesis is that these positions account for two qualitatively diverse clinical and epistemological matrices, linked to major transformations in health sciences and in psychiatry. We show that this debate involved a profound renewal of the meaning of psychiatric practice, a change in the function of diagnosis and in the way of conceiving the etiology of mental disorders, as well as a reformulation of the patient's suffering status for the medical act.

Trial of SAGE-217 in Patients with Major Depressive Disorder

BACKGROUND

Altered neurotransmission of γ-aminobutyric acid (GABA) has been implicated in the pathogenesis of depression. Whether SAGE-217, an oral, positive allosteric modulator of GABA type A receptors, is effective and safe for the treatment of major depressive disorder is unknown.

METHODS

In this double-blind, phase 2 trial, we enrolled patients with major depression and randomly assigned them in a 1:1 ratio to receive 30 mg of SAGE-217 or placebo once daily. The primary end point was the change from baseline to day 15 in the score on the 17-item Hamilton Depression Rating Scale (HAM-D; scores range from 0 to 52, with higher scores indicating more severe depression). Secondary efficacy end points, which were assessed on days 2 through 8 and on days 15, 21, 28, 35, and 42, included changes from baseline in scores on additional depression and anxiety scales, a reduction from baseline of more than 50% in the HAM-D score, a HAM-D score of 7 or lower, and a Clinical Global Impression of Improvement score of 1 (very much improved) or 2 (much improved) (on a scale of 1 to 7, with a score of 7 indicating that symptoms are very much worse).

RESULTS

A total of 89 patients underwent randomization: 45 patients were assigned to the SAGE-217 group, and 44 to the placebo group. The mean baseline HAM-D score was 25.2 in the SAGE-217 group and 25.7 in the placebo group. The least-squares mean (±SE) change in the HAM-D score from baseline to day 15 was -17.4±1.3 points in the SAGE-217 group and -10.3±1.3 points in the placebo group (least-squares mean difference in change, -7.0 points; 95% confidence interval, -10.2 to -3.9; P<0.001). The differences in secondary end points were generally in the same direction as those of the primary end point. There were no serious adverse events. The most common adverse events in the SAGE-217 group were headache, dizziness, nausea, and somnolence.

CONCLUSIONS

Administration of SAGE-217 daily for 14 days resulted in a reduction in depressive symptoms at day 15. Adverse events were more common in the SAGE-217 group than in the placebo group. Further trials are needed to determine the durability and safety of SAGE-217 in major depressive disorder and to compare SAGE-217 with available treatments. (Funded by Sage Therapeutics; ClinicalTrials.gov number, NCT03000530.).

Classifying Depression Severity in Recovery From Major Depressive Disorder via Dynamic Facial Features

Major depressive disorder is a common psychiatric illness. At present, there are no objective, non-verbal, automated markers that can reliably track treatment response. Here, we explore the use of video analysis of facial expressivity in a cohort of severely depressed patients before and after deep brain stimulation (DBS), an experimental treatment for depression. We introduced a set of variability measurements to obtain unsupervised features from muted video recordings, which were then leveraged to build predictive models to classify three levels of severity in the patients' recovery from depression. Multiscale entropy was utilized to estimate the variability in pixel intensity level at various time scales. A dynamic latent variable model was utilized to learn a low-dimensional representation of factors that describe the dynamic relationship between high-dimensional pixels in each video frame and over time. Finally, a novel elastic net ordinal regression model was trained to predict the severity of depression, as independently rated by standard rating scales. Our results suggest that unsupervised features extracted from these video recordings, when incorporated in an ordinal regression predictor, can discriminate different levels of depression severity during ongoing DBS treatment. Objective markers of patient response to treatment have the potential to standardize treatment protocols and enhance the design of future clinical trials.

How to determine whether conceptual endophenotypes can improve clinical outcomes in patients suffering from major depression: An exploratory approach

Depression is a complex mental health disorder, resulting in a high degree of disability. Since symptom constellation, course, and outcome are heterogeneous in these patients, current research initiatives are striving to establish stratified diagnostic and treatment approaches. In the past two decades, Dirk Hellhammer and his team introduced Neuropattern, a new diagnostic concept, which is based on conceptual endophenotypes of the stress response network. We explore how to use this concept in clinical practice in order to ultimately determine whether it brings any value over standard care. In view of the novelty of the concept and the difficulties dealing with such a concept at a practical level, it was necessary to initiate an exploratory study to determine key factors for planning future clinical trials. We report results and knowledge gained from an exploratory single-site study investigating the use and potential benefits of Neuropattern in standard care. Inpatients (ICD-10 diagnosis F32, F33; Nö=ö178) were allocated to either treatment as usual (standard group, SG) or a novel Neuropattern oriented exploratory treatment (intervention group, IG). Symptom severity was assessed with psychometric tests at admission to hospital, during the first six weeks, and upon discharge from the hospital. In addition, direct and indirect costs were assessed for the 3-month-intervals prior to and after the hospital stay. Compared to the SG, depression scores of patients in the IG showed a faster decline once psychotherapeutic and pharmacological treatment were based on an individualized explanatory model. The patients in the IG with an F33 diagnosis showed a more pronounced reduction of depression severity during the stay in the hospital and a stronger and quicker reduction of general symptom severity. Comparing the average depression scores at the start of the study and after six weeks, symptom severity was reduced in all Neuropattern groups. Some limitations of the study have to be mentioned: The study was not blinded, was single-site, included highly depressed inpatients only, and was conducted for no longer than 8 months. The results highlight some important issues regarding taking the Neuropattern approach to the bedside and researching its efficacy and effectiveness to support personalized treatments in clinical care.

Uncovering the Genetic Architecture of Major Depression

There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.

Reverse GWAS: Using genetics to identify and model phenotypic subtypes

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.

Historical precedents for the DSM-III bereavement exclusion criteria for major depression

BACKGROUND

I review the historical antecedents of the two key features of the bereavement exclusion (BE) for major depression (MD) criteria initially proposed in DSM-III: (i) a context-dependent approach to the evaluation of MD which required that the diagnosis be given only when course, symptoms and signs are 'out of proportion' to experienced adversities, and (ii) bereavement is the sole adversity for which this context-dependent approach should be utilized.

METHODS

A review of 49 textbook and review articles on depression or melancholia published 1880-1960.

RESULTS

Seventeen (35%) of the 49 texts advocated for a context-dependent approach to the diagnosis of MD. Most advocates relied on an intuitive clinical understanding of when the depressive features were v. were not commensurate with the experienced adversities. Several authors suggested that specific symptoms or course of illness could differentiate MD from 'normative' sadness. Others noted that patient reports of psychological causes of their depression should be treated skeptically. While death of loved ones was the most frequently noted specific adversity associated with MD, no author considered it qualitatively different from other stressors or suggested that it alone should be considered when diagnosing MD in a context-dependent manner.

CONCLUSIONS

A key underlying assumption of the BE criteria - a context-dependent approach to the diagnosis of MD- was advocated by a significant minority of earlier psychiatric diagnosticians, although problems in its clinical implementation were sometimes noted. No historical precedent was found for the application of the context-dependent approach only to bereavement, as proposed in DSM-III.

Empirical evidence for discrete neurocognitive subgroups in patients with non-psychotic major depressive disorder: clinical implications

BACKGROUND

Neuropsychological deficits are present across various cognitive domains in major depressive disorder (MDD). However, a consistent and specific profile of neuropsychological abnormalities has not yet been established.

METHODS

We assessed cognition in 170 patients with non-psychotic MDD using the Brief Assessment of Cognition in Schizophrenia and the scores were compared with those of 42 patients with schizophrenia as a reference for severity of cognitive impairment. Hierarchical cluster analysis was conducted to determine whether there are discrete neurocognitive subgroups in MDD. We then compared the subgroups in terms of several clinical factors and social functioning.

RESULTS

Three distinct neurocognitive subgroups were found: (1) a mild impairment subgroup with near-normative performance and mild dysfunction in motor speed; (2) a selective impairment subgroup, which exhibited preserved working memory and executive function, but moderate to severe deficits in verbal memory, motor speed, verbal fluency, and attention/information processing speed; and (3) a global impairment subgroup with moderate to severe deficits across all neurocognitive domains, comparable with deficits in schizophrenia. The global impairment subgroup was characterized by lower pre-morbid intelligence quotient (IQ). Moreover, a significant difference between groups was observed in premorbid IQ (p = 0.003), antidepressant dose (p = 0.043), antipsychotic dose (p = 0.013), or anxiolytic dose (p < 0.001).

CONCLUSIONS

These results suggest the presence of multiple neurocognitive subgroups in non-psychotic MDD with unique profiles, one of which exhibits deficits comparable to those of schizophrenia. The results of the present study may help guide future efforts to target these disabling symptoms using different treatments.

fMRI classification method with multiple feature fusion based on minimum spanning tree analysis

Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.

A longitudinal study of women's depression symptom profiles during and after the postpartum phase

BACKGROUND

An issue of critical importance for psychiatry and women's health is whether postpartum depression (PPD) represents a unique condition. The Diagnostic and Statistical Manual of Mental Disorders asserts that major depressive disorder (MDD) may present with peripartum onset, without suggesting any other differences between MDD and PPD. The absence of any distinct features calls into question the nosologic validity of PPD as a diagnostic category. The present study investigates whether symptom profiles differ between PPD and depression occurring outside the postpartum phase.

METHODS

In a prospective, longitudinal study of parturient women (N = 239), we examine the manifestation of depression symptoms. We assess factor structure of symptom profiles, and whether factors are differentially pronounced during and after the postpartum period.

RESULTS

Factors were revealed representing: Worry, Emotional/Circadian/Energetic Dysregulation, Somatic/Cognitive, Appetite, Distress Display, and Anger symptoms. The factor structure was validated at postpartum and after-postpartum timepoints. Interestingly, the Worry factor, comprising anxiety and guilt, was significantly more pronounced during the postpartum timepoint, and the Emotional/Circadian/Energetic Dysregulation factor, which contained sadness and anhedonia, was significantly less pronounced during the postpartum period.

CONCLUSIONS

These results suggest that PPD may be a unique syndrome, necessitating research, diagnosis, and treatment strategies distinct from those for MDD. Results indicate the possibility that Worry is an enhanced feature of PPD compared to depression outside the postpartum period, and the crucial role of sadness/anhedonia in MDD diagnosis may be less applicable to PPD diagnosis.

Vascular Pathology and Trajectories of Late-Life Major Depressive Disorder in Secondary Psychiatric Care

OBJECTIVE

To examine 5-year trajectories of psychiatrist-treated late-life major depressive disorder (MDD), and evaluate whether previous vascular pathology is associated with more severe trajectories of late-life MDD.

METHODS

Data were obtained from nationally representative civil, psychiatric, hospital, and prescription registers in Denmark. The sample included 11,092 older adults (≥60 years) who received their first diagnosis of MDD in a psychiatric facility in Denmark between 2000 and 2007. Trajectories of inpatient or outpatient contact at psychiatric hospitals for MDD over the 5-year period following index MDD diagnosis were modeled using latent class growth analysis. Measures of vascular disease (stroke, heart disease, vascular dementia) and vascular risk factors (hypertension, diabetes) were defined based on medication prescriptions and hospital-based diagnoses. Other predictors included demographic characteristics and characteristics of the index MDD diagnosis.

RESULTS

The final model included 4 trajectories with consistently low (66% of the sample), high decreasing (19%), consistently high (9%), and moderate fluctuating (6%) probabilities of contact at a psychiatric hospital for MDD during the 5-year period following the index MDD diagnosis. We found no significant associations between any form of vascular pathology and trajectory class membership. Relative to the consistently low class, older age, greater severity and >12 months of prior antidepressant medication use predicted membership in the other three classes.

CONCLUSIONS

A notable proportion (34%) of individuals diagnosed with MDD in late-life require secondary psychiatric treatment for extended time periods. We did not find evidence that vascular pathology predicts hospital contact trajectories in secondary-treated late-life MDD.

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

Depressive subtypes in an elderly cohort identified using latent class analysis

BACKGROUND

Clinical findings indicate heterogeneity of depressive disorders, stressing the importance of subtyping depression for research and clinical care. Subtypes of the common late life depression are however seldom studied. Data-driven methods may help provide a more empirically-based classification of late-life depression.

METHODS

Data were used from the Netherlands Study of Depression in Older People (NESDO) derived from 359 persons, aged 60 years or older, with a current diagnosis of major depressive disorder. Latent class analysis (LCA) was used to identify subtypes of depression, using ten CIDI-based depression items. Classes were then characterized using various sociodemographic and clinical characteristics.

RESULTS

The most prevalent class, as identified by LCA, was a moderate-severe class (prevalence 46.5%), followed by a severe melancholic class (prevalence 38.4%), and a severe atypical class (prevalence 15.0%). The strongest distinguishing features between the three classes were appetite and weight and, to a lesser extent, psychomotor symptoms and loss of interest. Compared with the melancholic class, the severe atypical class had the highest prevalence of females, the lowest mean age, the highest BMI, and highest prevalence of both cardiovascular disease, and metabolic syndrome.

LIMITATIONS

The strongest distinguishing symptoms, appetite and weight, could be correlated. Further, only longitudinal studies could demonstrate whether the identified classes are stable on the long term.

DISCUSSION

In older persons with depressive disorders, three distinct subtypes were identified, similar to subtypes found in younger adults. The strongest distinguishing features were appetite and weight; moreover, classes differed strongly on prevalence of metabolic syndrome and cardiovascular disease. These findings suggest differences in the involvement of metabolic pathways across classes, which should be considered when investigating the pathogenesis and (eventually) treatment of depression in older persons.

A latent genetic subtype of major depression identified by whole-exome genotyping data in a Mexican-American cohort

Identifying data-driven subtypes of major depressive disorder (MDD) is an important topic of psychiatric research. Currently, MDD subtypes are based on clinically defined depression symptom patterns. Although a few data-driven attempts have been made to identify more homogenous subgroups within MDD, other studies have not focused on using human genetic data for MDD subtyping. Here we used a computational strategy to identify MDD subtypes based on single-nucleotide polymorphism genotyping data from MDD cases and controls using Hamming distance and cluster analysis. We examined a cohort of Mexican-American participants from Los Angeles, including MDD patients (n=203) and healthy controls (n=196). The results in cluster trees indicate that a significant latent subtype exists in the Mexican-American MDD group. The individuals in this hidden subtype have increased common genetic substrates related to major depression and they also have more anxiety and less middle insomnia, depersonalization and derealisation, and paranoid symptoms. Advances in this line of research to validate this strategy in other patient groups of different ethnicities will have the potential to eventually be translated to clinical practice, with the tantalising possibility that in the future it may be possible to refine MDD diagnosis based on genetic data.

The recovery index: A novel approach to measuring recovery and predicting remission in major depressive disorder

BACKGROUND

Clinicians view "recovery" as the reduction in severity of symptoms over time, whereas patients view it as the restoration of premorbid functioning level and quality of life (QOL). The main purpose of this study is to incorporate patient-reported measures of functioning and QOL into the assessment of patient outcomes in MDD and to use this data to define recovery.

METHOD

Using the STAR*D study of patients diagnosed with MDD, this present analysis grades patients' MDD severity, functioning level, and QOL at exit from each level of the study, as well as at follow-up. Using Item Response Theory, we combined patient data from functioning and QOL measures (WSAS, Q-LES-Q) in order to form a single latent dimension named the Recovery Index.

RESULTS

Recovery Index - a latent measure assessing impact of illness on functioning and QOL - is able to predict remission of MDD in patients who participated in the STAR*D study.

CONCLUSIONS

By incorporating functioning and quality of life, the Recovery index creates a new dimension towards measuring restoration of health, in order to move beyond basic symptom measurement.

Childhood depressive disorders

Major depressive disorder (MDD) is a frequent and painful mental disorder considered among the five leading causes of disability in Western countries by the World Health Organization. MDD occurs at all ages, but childhood onset MDD has a more severe course with longer depressive episodes, more suicidality, and more frequent hospitalization, than later onset MDD. Childhood seems to be a window of opportunity for prevention of mental disorders, and subsequently prevention of MDD onset in childhood is recommended. Feasible prevention targets either individuals who present early signs of a given disorder but have not reached diagnostic threshold (indicated prevention) or individuals who are at increased risk for a disorder due to risk factor exposure (selective prevention). Indicated prevention is rational also for depressive disorders, because subthreshold depression (SD) in adults is found to be a precursor to MDD. The purpose of this thesis was to provide information necessary for the prevention of MDD onset in childhood. First, we examined whether the literature supports that SD is a MDD precursor also in children (systematic review). Second, we explored the risk that gender might constitute for pre-pubertal and post-pubertal onset MDD (register study). Third, we estimated the prevalence of SD and MDD in a large-scale pre-pubertal sample, and compared the clinical features of SD and MDD and potential risk factors (population-based study). The systematic review of the literature showed that SD in children and adolescents presents analogous comorbidity and symptom patterns (including self-harm symptoms). It also supports that SD is a precursor to MDD in children and adolescents causing poor outcomes like psychopathology, functional impairment and high use of health service. In the register study of Danish children and adolescents, we found a higher incidence of clinical MDD for girls after puberty compared to boys. Before puberty however, we demonstrated that boys had higher MDD incidence rates than girls. The population-based study including 3,421 8-10-year-old children from the Danish National Birth Cohort (DNBC) showed point prevalence estimates of 0.5% for MDD and 1.0% for SD. Children with SD by definition hold fewer depressive symptoms, but the ranking and frequency of these individual depressive symptoms was almost similar. Only irritability, anhedonia and worthlessness/guilt were more common in children with MDD. DNBC children with SD and MDD had comorbid anxiety or conduct/oppositional disorders just as frequently, and the degree of functional impairment was the same. When examining potential risk factors for SD and MDD, we found that poor general health, more than two stressful life events (SLE) within the past year, and a high level of maternal depressive symptoms were correlated to both SD and MDD. In addition we found epilepsy/convulsions, one SLE within the past year and parental divorce/separation to be correlated to MDD. In conclusion, the findings reported in this thesis underline that SD in childhood and adolescence is a significant condition calling for attention, due to the early onset, the risk for progression into MDD and the poor outcome. Indicated prevention aimed at MDD in childhood should target SD children who are characterised by fewer depressive symptoms but the same symptom pattern, the same level of impairment, and the same amount of comorbid anxiety and conduct/oppositional disorders, as presented by children with MDD. Selective preventive interventions could effectively target children who suffer from chronic physical illness and children whose mothers present depressive symptoms, also below clinical threshold. In addition, boys might have an increased risk for developing pre-pubertal MDD, but this has to be explored further in non-clinical samples. We recommend that more attention is paid to children and adolescents with subthreshold depressive symptoms who also pre-sent significant functional impairment. Emphasis must be put on the risk for SD transforming into MDD, especially in those exposed to the potential risk factors identified in this thesis.

Reevaluating the prevalence and diagnostic subtypes of depressive disorders in epilepsy

OBJECTIVE

Depressive disorders are common among patients with epilepsy (PWE). The aim of this study was to estimate the prevalence of different forms of depressive disorders among PWE treated in the outpatient setting.

METHODS

A group of consecutive PWE that visited the epilepsy outpatient clinic was invited to participate in the study. Ninety-six patients met inclusion criteria and were examined by a trained psychiatrist using standardized measures.

RESULTS

A diagnosis of a current major depression was established in 21 (22.3%) out of 96 participants. Furthermore, almost 20% of the study group fulfilled criteria for mood disorder categories other than MDD, adding up to over 40% of PWE suffering from any mood disorder category. Older age and later age at seizure onset, as well as unemployment, were associated with an increase in the odds of MDD diagnosis.

STUDY LIMITATIONS

A number of limitations are to be considered: the sample size is relatively small, and the findings may not be representative of PWE in general because our population represents a sample coming from a single outpatient clinic with a higher ratio of drug-resistant epilepsy.

CONCLUSIONS

Major depression as well as other forms of depressive disorders are common among PWE. Unemployment, age, and age at seizure onset are important factors associated with major depression among PWE.

Are there different genotypes in Bipolar II and Bipolar I disorder and if so, why then do we tend to observe Unipolar Depression converting to Bipolar II and then converting to Bipolar I?

We review the recent literature in order to establish the importance of a spectrum for bipolar affective disorder, and that unipolar depression, bipolar II and bipolar I are discrete entities that may however evolve in sequence. We discuss clinical, genetic and neurobiological data which illustrate the differences between bipolar I and bipolar II. To fit the data we suggest a series of multiple mood disorder genotypes, some of which evolve into other conditions on the bipolar spectrum. Thence we discuss the nature of the bipolar spectrum and demonstrate how this concept can be used as the basis of a staging model for bipolar disorder.

Demoralization: a systematic review on its clinical characterization

BACKGROUND

Demoralization has been described as a psychological state characterized by helplessness, hopelessness, a sense of failure and the inability to cope.

METHODS

We conducted a systematic review with qualitative data analysis following PRISMA criteria with the following aims: to review validated assessment instruments of the demoralization syndrome, report main findings regarding demoralization as measured by validated instruments that emerge in the literature, compare and report evidence for the clinical utility of the identified instruments. Utilizing the key word 'demoralization' in PubMed and PsycINFO databases, an electronic search was performed, supplemented by Web of Science and manual searches. Study selection criteria included the assessment of medical patients and use of instruments validated to assess demoralization. Seventy-four studies were selected.

RESULTS

Four instruments emerged in the literature. Main findings concern prevalence rates of demoralization, evidence of discriminant validity from major depression, factors associated with demoralization and evidence of clinical utility. The instruments vary in their definition, the populations they aim to assess, prevalence rates they estimate and their ability to discriminate between different conditions. Nonetheless, demoralization appears to be a distinctive psychological state characterized by helplessness, hopelessness, giving up and subjective incompetence. It is not limited to life-threatening diseases such as cancer, but may occur in any type of clinical situation. It is associated with stress and adverse health outcomes.

CONCLUSIONS

Studies addressing the incremental value of demoralization in psychiatry and psychology are needed. However, demoralization appears to entail specific clinical features and may be a distinct condition from major depression.

DSM-5-defined 'mixed features' and Benazzi's mixed depression: which is practically useful to discriminate bipolar disorder from unipolar depression in patients with depression?

AIMS

Irritability, psychomotor agitation, and distractibility in a major depressive episode (MDE) should not be counted as manic/hypomanic symptoms of DSM-5-defined mixed features; however, this remains controversial. The practical usefulness of this definition in discriminating bipolar disorder (BP) from major depressive disorder (MDD) in patients with depression was compared with that of Benazzi's mixed depression, which includes these symptoms.

METHODS

The prevalence of both definitions of mixed depression in 217 patients with MDE (57 bipolar II disorder, 35 BP not otherwise specified, and 125 MDD cases), and their operating characteristics regarding BP diagnosis were compared.

RESULTS

The prevalence of both Benazzi's mixed depression and DSM-5-defined mixed features was significantly higher in patients with BP than it was in patients with MDD, with the latter being quite low (62.0% vs 12.8% [P < 0.0001], and 7.6% vs 0% [P < 0.0021], respectively). The area under the receiver operating curve for BP diagnosis according to the number of all manic/hypomanic symptoms was numerically larger than that according to the number of manic/hypomanic symptoms excluding the above-mentioned three symptoms (0.798; 95% confidence interval, 0.736-0.859 vs 0.722; 95% confidence interval, 0.654-0.790). The sensitivity/specificity of DSM-5-defined mixed features and Benazzi's mixed depression for BP diagnosis were 5.1%/100% and 55.1%/87.2%, respectively.

CONCLUSIONS

DSM-5-defined mixed features were too restrictive to discriminate BP from MDD in patients with depression compared with Benazzi's definition. To confirm this finding, studies that include patients with BP-I and using tools to assess manic/hypomanic symptoms during MDE are necessary.

Subtypes of major depression: latent class analysis in depressed Han Chinese women

BACKGROUND

Despite substantial research, uncertainty remains about the clinical and etiological heterogeneity of major depression (MD). Can meaningful and valid subtypes be identified and would they be stable cross-culturally?

METHOD

Symptoms at their lifetime worst depressive episode were assessed at structured psychiatric interview in 6008 women of Han Chinese descent, age ⩾ 30 years, with recurrent DSM-IV MD. Latent class analysis (LCA) was performed in Mplus. RESULTS; Using the nine DSM-IV MD symptomatic A criteria, the 14 disaggregated DSM-IV criteria and all independently assessed depressive symptoms (n = 27), the best LCA model identified respectively three, four and six classes. A severe and non-suicidal class was seen in all solutions, as was a mild/moderate subtype. An atypical class emerged once bidirectional neurovegetative symptoms were included. The non-suicidal class demonstrated low levels of worthlessness/guilt and hopelessness. Patterns of co-morbidity, family history, personality, environmental precipitants, recurrence and body mass index (BMI) differed meaningfully across subtypes, with the atypical class standing out as particularly distinct.

CONCLUSIONS

MD is a clinically complex syndrome with several detectable subtypes with distinct clinical and demographic correlates. Three subtypes were most consistently identified in our analyses: severe, atypical and non-suicidal. Severe and atypical MD have been identified in multiple prior studies in samples of European ethnicity. Our non-suicidal subtype, with low levels of guilt and hopelessness, may represent a pathoplastic variant reflecting Chinese cultural influences.

Does age at onset of first major depressive episode indicate the subtype of major depressive disorder?: the clinical research center for depression study

PURPOSE

The purpose of this study was to evaluate the effects of age at onset of the first major depressive episode on the clinical features of individuals with major depressive disorder (MDD) in a large cohort of Korean depressed patients.

MATERIALS AND METHODS

We recruited 419 MDD patients of age over 18 years from the Clinical Research Center for Depression study in South Korea. At the start of the study, the onset age of the first major depressive episode was self-reported by the subjects. The subjects were divided into four age-at-onset subgroups: childhood and adolescent onset (ages <18), early adult onset (ages 18-44), middle adult onset (ages 45-59), and late onset (ages 60+). Using analysis of covariance (ANCOVA) and ordinal logistic regression analysis with adjusting the effect of age, the relationships between clinical features and age at onset of MDD were evaluated.

RESULTS

There was an apparent, but inconsistent correlation between clinical features and age at onset. Earlier onset MDD was significantly associated with higher proportion of female gender [adjusted odds ratio (AOR)=0.570, p=0.022], more previous suicide attempts (AOR=0.635, p=0.038), greater number of previous depressive episodes (F=3.475, p=0.016) and higher scores on the brief psychiatric rating scale (F=3.254, p=0.022), its negative symptom subscale (F=6.082, p<0.0001), and the alcohol use disorder identification test (F=7.061, p<0.0001).

CONCLUSION

Early age at onset may increase the likelihood of distinguishable MDD subtype, and age at onset of the first major depressive episode is a promising clinical indicator for the clinical presentation, course, and outcome of MDD.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

BACKGROUND

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.

METHOD

Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.

RESULTS

Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.

CONCLUSIONS

Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Prognostic subgroups for citalopram response in the STAR*D trial

OBJECTIVE

Few data exist to help clinicians predict likelihood of treatment response in individual patients with major depressive disorder (MDD). Our aim was to identify subgroups of MDD patients with differential treatment outcomes based on presenting clinical characteristics. We also sought to quantify the likelihood of treatment success based on the degree of improvement and side effects after 2 and 4 weeks of selective serotonin reuptake inhibitor (SSRI) pharmacotherapy.

METHOD

We analyzed data from the first treatment phase of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, in which subjects with a DSM-IV diagnosis of MDD were treated for 8-14 weeks with open-label citalopram. A receiver operating characteristic (ROC) analysis was conducted to determine homogenous subgroups with different rates of response and remission in depressive symptoms. Included predictor variables were initial clinical characteristics, initial improvement, and side effects after 2 and 4 weeks of SSRI treatment. The primary outcome measures were treatment response (defined as a greater than 50% reduction in 17-item Hamilton Depression Rating Scale [HDRS-17] score from baseline) and remission (defined as an HDRS-17 score ≤ 17).

RESULTS

Baseline clinical characteristics were able to identify subgroups from a low likelihood of response of 18% (income < $10,000, comorbid generalized anxiety disorder, < 16 years of education; P < .01) to a high likelihood of response of 68% (income ≥ $40,000, no comorbid posttraumatic stress disorder; P < .01). Among baseline clinical characteristics, employment status (N = 2,477; χ²₁ = 78.1; P < .001) and income level (N = 2,512; χ²₁ = 77.7; P < .001) were the most informative in predicting treatment outcome. For the models at weeks 2 and 4, treatment success was best predicted by early symptom improvement.

CONCLUSIONS

Socioeconomic data such as low income, education, and unemployment were most discriminative in predicting a poor response to citalopram, even with disparities in access to care accounted for. This finding implies that socioeconomic factors may be more useful predictors of medication response than traditional psychiatric diagnoses or past treatment history.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT00021528.

The expanding cognitive-behavioural therapy treatment umbrella for the anxiety disorders: disorder-specific and transdiagnostic approaches

Cognitive-behavioural therapy (CBT) is an empirically supported treatment for anxiety disorders. CBT treatments are based on disorder-specific protocols that have been developed to target individual anxiety disorders, despite that anxiety disorders frequently co-occur and are comorbid with depression. Given the high rates of diagnostic comorbidity, substantial overlap in dimensional symptom ratings, and extensive evidence that the mood and anxiety disorders share a common set of psychological and biological vulnerabilities, transdiagnostic CBT protocols have recently been developed to treat the commonalities among the mood and anxiety disorders. We conducted a selective review of empirical developments in the transdiagnostic CBT treatment of anxiety and depression (2008-2013). Preliminary evidence suggests that theoretically based transdiagnostic CBT approaches lead to large treatment effects on the primary anxiety disorder, considerable reduction of diagnostic comorbidity, and some preliminary effects regarding the impact on the putative, shared psychological mechanisms. However, the empirical literature remains tentative owing to relatively small samples, limited direct comparisons with disorder-specific CBT protocols, and the relative absence of the study of disorder-specific compared with shared mechanisms of action in treatment. We conclude with a treatment conceptualization of the new transdiagnostic interventions as complementary, rather than contradictory, to disorder-specific CBT.

The endogenous and reactive depression subtypes revisited: integrative animal and human studies implicate multiple distinct molecular mechanisms underlying major depressive disorder

BACKGROUND

Traditional diagnoses of major depressive disorder (MDD) suggested that the presence or absence of stress prior to onset results in either 'reactive' or 'endogenous' subtypes of the disorder, respectively. Several lines of research suggest that the biological underpinnings of 'reactive' or 'endogenous' subtypes may also differ, resulting in differential response to treatment. We investigated this hypothesis by comparing the gene-expression profiles of three animal models of 'reactive' and 'endogenous' depression. We then translated these findings to clinical samples using a human post-mortem mRNA study.

METHODS

Affymetrix mouse whole-genome oligonucleotide arrays were used to measure gene expression from hippocampal tissues of 144 mice from the Genome-based Therapeutic Drugs for Depression (GENDEP) project. The study used four inbred mouse strains and two depressogenic 'stress' protocols (maternal separation and Unpredictable Chronic Mild Stress) to model 'reactive' depression. Stress-related mRNA differences in mouse were compared with a parallel mRNA study using Flinders Sensitive and Resistant rat lines as a model of 'endogenous' depression. Convergent genes differentially expressed across the animal studies were used to inform candidate gene selection in a human mRNA post-mortem case control study from the Stanley Brain Consortium.

RESULTS

In the mouse 'reactive' model, the expression of 350 genes changed in response to early stresses and 370 in response to late stresses. A minimal genetic overlap (less than 8.8%) was detected in response to both stress protocols, but 30% of these genes (21) were also differentially regulated in the 'endogenous' rat study. This overlap is significantly greater than expected by chance. The VAMP-2 gene, differentially expressed across the rodent studies, was also significantly altered in the human study after correcting for multiple testing.

CONCLUSIONS

Our results suggest that 'endogenous' and 'reactive' subtypes of depression are associated with largely distinct changes in gene-expression. However, they also suggest that the molecular signature of 'reactive' depression caused by early stressors differs considerably from that of 'reactive' depression caused by late stressors. A small set of genes was consistently dysregulated across each paradigm and in post-mortem brain tissue of depressed patients suggesting a final common pathway to the disorder. These genes included the VAMP-2 gene, which has previously been associated with Axis-I disorders including MDD, bipolar depression, schizophrenia and with antidepressant treatment response. We also discuss the implications of our findings for disease classification, personalized medicine and case-control studies of MDD.

Proposed DSM-5 mixed features are associated with greater likelihood of remission in out-patients with major depressive disorder

BACKGROUND

Draft DSM-5 criteria for a mixed major depressive episode have been proposed, but their predictive validity has not yet been established. We hypothesized that such symptoms would be associated with poorer antidepressant treatment outcomes.

METHOD

We examined outcomes among individuals with major depressive disorder participating in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, an effectiveness study conducted at primary and specialty care centers in the USA. Mixed features were derived from the six self-report items of the mania subscale of the Psychiatric Diagnosis Screening Questionnaire. Primary analyses examined the association between the presence of at least two of these in the 6 months before study entry, and remission across up to four sequential treatment trials, as well as adverse outcomes.

RESULTS

Of the 2397 subjects with a major depressive episode of at least 6 months' duration, 449 (18.7%) reported at least two mixed symptoms. The presence of such symptoms was associated with a greater likelihood of remission across up to four sequential treatments, which persisted after adjustment for potential confounding clinical and demographic variables (adjusted hazard ratio 1.16, 95% confidence interval 1.03-1.28). Two individual items, expansive mood and cheerfulness, were strongly associated with a greater likelihood of remission.

CONCLUSIONS

Proposed DSM-5 mixed state features were associated with a greater rather than a lesser likelihood of remission. While unexpected, this result suggests the potential utility of further investigation of depressive mixed states in major depression.

Anxious and non-anxious forms of major depression: familial, personality and symptom characteristics

BACKGROUND

Earlier clinical studies have suggested consistent differences between anxious and non-anxious depression. The aim of this study was to compare parental pathology, personality and symptom characteristics in three groups of probands from the general population: depression with and without generalized anxiety disorder (GAD) and with other anxiety disorders. Because patients without GAD may have experienced anxious symptoms for up to 5 months, we also considered GAD with a duration of only 1 month to produce a group of depressions largely unaffected by anxiety.

METHOD

Depressive and anxiety disorders were assessed in a 10-year prospective longitudinal community and family study using the DSM-IV/M-CIDI. Regression analyses were used to reveal associations between these variables and with personality using two durations of GAD: 6 months (GAD-6) and 1 month (GAD-1).

RESULTS

Non-anxious depressives had fewer and less severe depressive symptoms, and higher odds for parents with depression alone, whereas those with anxious depression were associated with higher harm avoidance and had parents with a wider range of disorders, including mania.

CONCLUSIONS

Anxious depression is a more severe form of depression than the non-anxious form; this is true even when the symptoms required for an anxiety diagnosis are ignored. Patients with non-anxious depression are different from those with anxious depression in terms of illness severity, family pathology and personality. The association between major depression and bipolar disorder is seen only in anxious forms of depression. Improved knowledge on different forms of depression may provide clues to their differential aetiology, and guide research into the types of treatment that are best suited to each form.

The mistreatment of major depressive disorder

OBJECTIVE

To examine the effects of classification on treatment in major depressive disorder (MDD).

METHOD

This is a narrative review.

RESULTS

MDD is a highly heterogeneous category, leading to problems in classification and in specificity of treatment. Current models classify all depressions within a single category. However, the construct of MDD obscures important differences between severe disorders that require pharmacotherapy, and mild-to-moderate disorders that can respond to psychotherapy or remit spontaneously. Patients with mild-to-moderate MDD are being treated with routine or overly aggressive pharmacotherapy.

CONCLUSIONS

The current classification fails to address the heterogeneity of depression, leading to mistreatment.

Melancholic and reactive depression: a reappraisal of old categories

BACKGROUND

The dominant diagnostic model of the classification of depression today is unitarian; however, since Kurt Schneider (1920) introduced the concept of endogenous depression and reactive depression, the binary model has still often been used on a clinical basis. Notwithstanding this, to our knowledge, there have been no collective data on how psychiatrists differentiate these two conditions. We therefore conducted a survey to examine how psychiatrists in Japan differentiate patients with major depressive disorder who present mainly with melancholic features and those with reactive features.

METHODS

Three case scenarios of melancholic and reactive depression, and one-in-between were prepared. These cases were designed to present with at least 5 symptoms listed in the DSM-IV-TR with severity being mild. We have sent the questionnaires regarding treatment options and diagnosis for those three cases on a 7-point Likert scale (1 = "not appropriate", 4 = "cannot tell", and 7 = "appropriate"). Five hundred and two psychiatrists from over one hundred hospitals and community clinics throughout Japan have participated in this survey.

RESULTS

The melancholic case resulted significantly higher than the reactive case on either antidepressants (mean ± SD: 5.9 ± 1.2 vs. 3.6 ± 1.7, p < 0.001), hypnotics (mean ± SD: 5.5 ± 1.1 vs. 5.0 ± 1.3, p < 0.001), and electroconvulsive therapy (mean ± SD: 1.5 ± 0.9 vs. 1.2 ± 0.6, p < 0.001). On the other hand, the reactive case resulted in significantly higher scores compared to the melancholic case and the one- in-between cases in regards to psychotherapy (mean ± SD: 4.9 ± 1.4 vs. 4.3 ± 1.4 vs. 4.7 ± 1.5, p < 0.001, respectively). Scores for informing patients that they suffered from "depression" were significantly higher in the melancholic case, compared to the reactive case (mean ± SD: 4.7 ± 1.7 vs. 2.2 ± 1.4, p < 0.001).

CONCLUSIONS

Japanese psychiatrists distinguish between major depressive disorder with melancholic and reactive features, and thus choose different treatment strategies regarding pharmacological treatment and psychotherapy.

Cognitive impairment in euthymic major depressive disorder: a meta-analysis

BACKGROUND

There is evidence to suggest that cognitive deficits might persist beyond the acute stages of illness in major depressive disorder (MDD). However, the findings are somewhat inconsistent across the individual studies conducted to date. Our aim was to conduct a systematic review and meta-analysis of existing studies that have examined cognition in euthymic MDD patients.

METHOD

Following a systematic search across several publication databases, meta-analyses were conducted for 27 empirical studies that compared euthymic adult MDD patients (895 participants) and healthy controls (997 participants) across a range of cognitive domains. The influence of demographic variables and confounding factors, including age of onset and recurrent episodes, was examined.

RESULTS

Compared with healthy controls, euthymic MDD patients were characterized by significantly poorer cognitive functions. However, the magnitude of observed deficits, with the exception of inhibitory control, were generally modest when late-onset cases were excuded. Late-onset cases demonstrated significantly more pronounced deficits in verbal memory, speed of information processing and some executive functions.

CONCLUSIONS

Cognitive deficits, especially poor response inhibition, are likely to be persistent features, at least of some forms, of adult-onset MDD. More studies are necessary to examine cognitive dysfunction in remitted psychotic, melancholic and bipolar spectrum MDD. Cognitive deficits overall appear to be more common among patients with late-onset depression, supporting the theories suggesting that possible vascular and neurodegenerative factors play a role in a substantial number of these patients.

The clinical utility of different quantitative methods for measuring treatment resistance in major depression

BACKGROUND

Despite the acknowledged healthcare and economic burdens of chronic major depression, there is no agreed method to rate the degree to which patients are conceptualised as being refractory to treatment. There are a variety of tools which can be used to describe treatment resistance but their utility in clinical practice is uncertain.

METHODS

We used a range of contemporary tools to rate the treatment histories of patients in a variety of care settings which included: primary care; affective disorders specialist clinics; patients receiving ECT; referrals to a tertiary affective disorders service; and patients undergoing neurosurgical treatment (vagus nerve stimulation or anterior cingulotomy) for chronic, refractory major depression.

RESULTS

All tools demonstrated statistically significant differences in scores between care settings, as well as between tiers of service, although differences between some groups were small and confidence intervals were wide. The Massachusetts General Hospital staging method appeared to perform as well as more complex scoring methods and represents a reasonable compromise between time to complete and its ability to inform management decisions.

LIMITATIONS

Numbers in some groups were low, but are likely to be representative. The ability of such tools to predict outcome was not examined and the proposed cut-offs require validation.

CONCLUSIONS

Currently available staging methods appear to have the ability to differentiate between clinically-relevant sub-groups of patients with major depression. Further development of such tools is warranted due to their ability to not only describe characteristics of patients in different care settings, but also meet the need to have meaningful cut-offs which might guide referral to specialist treatment.

Severity classification on the Hamilton Depression Rating Scale

BACKGROUND

Symptom severity as a moderator of treatment response has been the subject of debate over the past 20 years. Each of the meta- and mega-analyses examining the treatment significance of depression severity used the Hamilton Depression Rating Scale (HAMD), wholly, or in part, to define severity, though the cutoff used to define severe depression varied. There is limited empirical research establishing cutoff scores for bands of severity on the HAMD. The goal of the study is to empirically establish cutoff scores on the HAMD in their allocation of patients to severity groups.

METHODS

Six hundred twenty-seven outpatients with current major depressive disorder were evaluated with a semi-structured diagnostic interview. Scores on the 17-item HAMD were derived from ratings according to the conversion method described by Endicott et al. (1981). The patients were also rated on the Clinical Global Index of Severity (CGI). Receiver operating curves were computed to identify the cutoff that optimally discriminated between patients with mild vs. moderate and moderate vs. severe depression.

RESULTS

HAMD scores were significantly lower in patients with mild depression than patients with moderate depression, and patients with moderate depression scored significantly lower than patients with severe depression. The cutoff score on the HAMD that maximized the sum of sensitivity and specificity was 17 for the comparison of mild vs. moderate depression and 24 for the comparison of moderate vs. severe depression.

LIMITATIONS

The present study was conducted in a single outpatient practice in which the majority of patients were white, female, and had health insurance. Although the study was limited to a single site, a strength of the recruitment procedure was that the sample was not selected for participation in a treatment study, and exclusion and inclusion criteria did not reduce the representativeness of the patient groups. The analyses were based on HAMD scores extracted from ratings on the SADS. However, we used Endicott et al.'s (1981) empirically established formula for deriving a HAMD score from SADS ratings, and our results concurred with other small studies of the mean and median HAMD scores in severity groups.

CONCLUSIONS

Based on this large study of psychiatric outpatients with major depressive disorder we recommend the following severity ranges for the HAMD: no depression (0-7); mild depression (8-16); moderate depression (17-23); and severe depression (≥24).